Understanding the Impacts of Connected Autonomous Vehicles … · 2019. 11. 27. · Although, there is a lot of research on different aspects of connected and autonomous vehicles

Understanding the Impacts of Connected

Autonomous Vehicles on Pedestrians with Visual

Impairment

by

Sina Azizisoldouz

A thesis submitted in conformity with the requirements

for the degree of Master of Applied Science

Graduate Department of Civil and Mineral Engineering

University of Toronto

© Copyright by Sina Azizisoldouz 2019

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Understanding the Impacts of Connected Autonomous Vehicles

on Pedestrians with Visual Impairment

Sina Azizisoldouz

Master of Applied Science

Department of Civil and Mineral Engineering

University of Toronto

2019

Abstract

This thesis develops a policy-framework that can be used for minimizing communication issues

between connected autonomous vehicles (CAVs) and visually impaired pedestrians. The existing

literature on CAVs is highly focused on different perspectives of the possible users of these

technologies. Since, this research uses a dataset collected through a tailor-made stated adaptation

survey among visually impaired pedestrians. The dataset includes the current mobility issues of

this community, their perceptions about CAVs, and a wide range of socioeconomic attributes.

Some evidence-based recommendations are provided on communication techniques, based on

the key findings of a series of structural equation models (SEM) and ordered logit models

estimated using the survey data. The results reveal that the low-noise issue of electric engines

influences visually impaired pedestrians’ safety and security in the contexts of CAVs. It shows

that respondents who rely on mobile applications and technology-based devices for navigating

purposes tend to trust in CAVs.

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Acknowledgment

I like to thank my supervisor, Professor Khandker Nurul Habib, for his guidance, dedication, and

support. Thanks for giving me a chance and believing in me. I have learned a lot, and thank you

for being a passionate leader.

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Table of Contents

Abstract ...................................................................................................................................................... ii

Acknowledgment .................................................................................................................................... iii

Table of Content ...................................................................................................................................... iv

List of Figures ............................................................................................................................................. iv

List of Tables ............................................................................................................................................. ivi

Introduction and Background ........................................................................................................... 1

1.1 Introduction ................................................................................................................................................................... 1

1.2 Background ................................................................................................................................................................... 3

1.2.1 General Mobility Issues of Vulnerable Road Users (VRUs) .................................................................................... 3

1.2.2 ITS application for Mobility Improvement of VRUs ................................................................................................ 5

1.2.3 AVs/CAVs and VRUs .............................................................................................................................................. 5

Literature Review ............................................................................................................................... 8

2.1 Mobility Issues of Visually Impaired People................................................................................................................. 8

2.2 Technology-based navigational aids for visually impaired individuals ......................................................................... 8

2.3 Socio-economic Aspects of Transportation-Related Issues among Visually Impaired Individuals ............................. 11

2.4 Research Questions ..................................................................................................................................................... 13

Survey Design .................................................................................................................................... 14

3.1 Survey Design ............................................................................................................................................................. 14

3.2 Descriptive Statistics ................................................................................................................................................... 16

Empirical Model ................................................................................................................................ 24

4.1 SEM models: ............................................................................................................................................................... 24

4.1.1 Communication Issues Associated with Safety and Security.................................................................................. 26

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4.1.2 Communication Issues Associated with Importance of Hearing............................................................................. 29

4.1.3 Mobility Issues Associated with Safety and Security, and the Importance of Hearing ........................................... 29

4.1.4 Socioeconomic Attributes ....................................................................................................................................... 29

4.1.5 Trust in CAV and Preference for Using CAV ........................................................................................................ 30

4.2 Ordered Logit models: ................................................................................................................................................. 30

4.2.1 The Level of Trust in CAVs ................................................................................................................................... 32

4.2.2 The Preference for Using CAVs ............................................................................................................................. 35

Policy Recommendations .................................................................................................................. 38

5.1 Introduction ................................................................................................................................................................. 38

5.2 Background ................................................................................................................................................................. 39

5.2.1 Smartphones as the Core of Communication Systems ............................................................................................ 39

5.2.2 Vehicle-to-vehicle (V2V) and Vehicle-to-infrastructure (V2I)............................................................................... 39

5.2.3 LED-enhanced Technologies .................................................................................................................................. 39

5.2.4 Auditory Feedback ................................................................................................................................................. 40

5.2.5 Safety Zone ............................................................................................................................................................. 40

5.2.6 Artificial Intelligence .............................................................................................................................................. 40

5.3 Matching Key Finding from the SEM and the Ordered Logit Models with Policy Recommendations ....................... 43

Conclusion ......................................................................................................................................... 45

References .......................................................................................................................................... 48

Appendix: Survey on Visually Impaired Individuals (SUVI) ....................................................... 57

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List of Figures

Figure 3-1 Comparison of the Estimated Population from CNIB and the Observed Dataset ........... 16

Figure 3-2 Preference for Using CAVs Versus Level of Trust and Employment Status .................... 19

Figure 3-3 Selected Types of Navigational Assistance or Device Respondents Use While Crossing

the Roads ................................................................................................................................................... 20

Figure 3-4 Knew about CAVs before the Survey Figure 3-5 Independence of Travelling ................. 20

Figure 3-6 Preferences for Communication with CAVs ........................................................................ 21

Figure 3-7 Mode Choice Behavior of the Respondents .......................................................................... 22

Figure 3-8 Preference for Using CAVs ................................................................................................... 23

Figure 3-9 Trust in CAVs ......................................................................................................................... 23

Figure 4-1 SEM model result summary for Canadian sample ............................................................. 27

Figure 4-2 SEM model summary results for the non-Canadian sample .............................................. 28

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List of Tables

TABLE 3-1 A few Attitudinal Questions ................................................................................................ 14

TABLE 3-2 Descriptive Statistics for Selected Variables ...................................................................... 16

TABLE 4-1 Description of Variables for the Final Models ................................................................... 25

TABLE 4-2 Description of the Key Variables ........................................................................................ 31

TABLE 4-3 Empirical Model for the Level of Trust in CAVs .............................................................. 33

TABLE 4-4 Empirical Model for Preference for using CAVs .............................................................. 36

TABLE 5-1 Summary of Existing Literature on Communication Techniques with CAVs ............... 41

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Introduction and Background

1.1 Introduction

Pedestrians are one of the major vulnerable road users [3]. In general, providing safety and

mobility improvements are less considered for non-road users compared to drivers and car

passengers [4]. According to Transport Canada, 18.9% of the road fatalities involved pedestrians

in 2016 [5]. The percentage of total pedestrians’ fatalities did not change significantly between

2012 and 2016, however, the total number of pedestrians killed in crashes declined [5].

Moreover, the data shows that the majority of fatalities contributes to older pedestrians [5].

Among Vulnerable Road Users (VRUs), disabled pedestrians experience higher risks of road

accidents. As such, visually impaired pedestrians are faced a higher risk regarding

communication with drivers on roads and cyclists on sidewalks. Therefore, perceptions of

visually impaired pedestrians with respect to connected and autonomous vehicles would be an

asset to prevent thinking again when these technologies become commonplace.

This is a serious research gap in the literature. In general, most studies on communication

techniques with AVs/CAVs consider sighted pedestrians. In addition, most recommended

techniques are focused on visual aids, such as LED light in front of the vehicle. To address this

research question, this thesis aims to investigate the perceptions of people with visual

impairment about CAVs and their preferences for communicating with these vehicles. In this

regard, a comprehensive survey was designed to examine the feedback from people with visual

impairment.

Technological and strategic advances in the automobile industry and transportation systems have

been improved drastically during recent years in terms of introducing to connected and

autonomous vehicles. Although, there is a lot of research on different aspects of connected and

autonomous vehicles include acceptance level, safety and security, congestion, environmental

characteristics, etc. [6]–[9]. However, most studies target car passengers’ perceptions and

attitudes regarding different characteristics of connected and autonomous vehicles.

One area that is very important to be considered by researchers, policymakers, and industry

professionals is understanding the impacts of CAVs on visually impaired pedestrians. Similarly,

it is a little known fact that how connected and autonomous vehicles will communicate with

other non-motorized road users such as pedestrians or other vulnerable road users when different

levels of automation (level 0 = no automation to level 4 = full automation [10]) become

commonplace [11], [12], [21]–[23], [13]–[20]. Different countries and manufacturers are also

working on the possible solutions regarding the communication techniques between pedestrians

and connected and autonomous vehicles [24]–[29]. All these studies focused on solutions that

consider sighted pedestrians, i.e. LED lights in front of vehicles that announce their current

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status, such as about to stop to give a right of way or it is on autonomous mode (when the full-

automation (level 4) is not achieved yet).

It is not clear that which approach would be practical to be used on a large scale. Despite, there

are only a few studies that focused on the possible communication techniques between visually

impaired pedestrians and CAV [30], [31], there is no evidence that industry professionals or

policymakers consider the impacts of CAV on visually impaired pedestrians and how they want

to communicate with these technologies. In addition, only a few studies investigated the

perceptions of visually impaired people on autonomous vehicles [32], [33]. As a result, there is a

research gap on the investigation of general views of visually impaired pedestrians on CAVs.

This thesis used the dataset that was collected through a survey among this community. The

survey was conducted by the University of Toronto and the Canadian National Institute for the

Blind (CNIB). This thesis also used two econometric model approaches, the Structural Equation

Modeling (SEM) and the Ordered Logit Model, for providing a policy-framework regarding the

communication techniques between CAVs and visually impaired pedestrians.

This thesis began with a brief introduction about general mobility issues of VRUs and the

impacts of Intelligent Transportation System (ITS) application for improving the safety and

mobility of VRUs. This provides a general perspective regarding VRUs’ mobility issues with a

higher focus on pedestrians followed by possible impacts of connected and autonomous vehicles

on VRUs. Chapter 2 represents the literature review on mobility issues of visually impaired

individuals in three subsections: Mobility Issues of Visually Impaired People, Technology-based

Navigational Aids for the Visually Impaired People, and Socio-economic Aspects of Visual

Impairment regarding Transportation-Related Issues.

There is ongoing research on different perspectives of connected and autonomous vehicles. A

wide range of studies conducted for sighted people’s perspectives such as acceptance level and

preferences of using these technologies, but there is very limited research on visually impaired

people regarding their perspectives on connected and autonomous vehicles [32], [33]. Since, this

thesis aims to investigate the perceptions of visually impaired people about connected and

autonomous vehicles regarding how they want to communicate with these technologies, how

much they trust these technologies, and how much they prefer to be the user of these

technologies. To this end, a comprehensive web-based survey was designed for the purpose of

this thesis with the Canadian National Institute for the Blind (CNIB) collaboration. Chapter 3

describes the survey design process, data collection, and a summary of descriptive results.

Chapter 4 represents a policy framework regarding the latest recommendations on

communication techniques between connected and autonomous vehicles with pedestrians.

Chapter 4 also describes the best matches between the existing literature and the key findings

from the econometric models’ results regarding the communication techniques with connected

and autonomous vehicles with pedestrians. Finally, chapter 5 concludes with a brief summary of

the key findings from both econometric models and descriptive analysis, which is followed by

some recommendations for future research directions. The following section provides a

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background on mobility issues of vulnerable road users (VRUs), the implication of ITS for

improving mobility of VRUs, and the interaction between AVs/CAVs with VRUs.

1.2 Background

1.2.1 General Mobility Issues of Vulnerable Road Users (VRUs)

According to the WHO (2014), VRUs are “Road users most at risk in traffic, such as pedestrians,

cyclists and public transport passengers. Children, older people, and disabled people may also be

included in this category.” [3]. Therefore, investigating the mobility and safety of VRUs is a

critical task for planners and decision-makers.

Finding accident trends and causes regarding available data can help planners and decision-

makers to not only improve the functionality of transportation systems but also provide a higher

level of mobility and safety for VRUs. In this regard, some studies investigated critical

accidents’ locations, involved VRUs, by implementing matching methods and naturalistic

observations, such as mounted-cameras [34]–[40]. According to the CARE1 data, most common

accidents for pedestrian contributed to crossing roads remotely [40]. Moreover, the majority of

them happened between 12 pm to 6 pm and in areas with speed limits lower than 50km/h [40].

Built environments play one of the major role in safety and mobility of VRUs. Stoker et al. [41]

examined the associated risks of built environments for pedestrians based on their perceptions

and preferences regarding the walking environment. As pedestrians’ behavior analysis is a

subjective matter, it would be difficult to provide a universal approach for improving safety

features of transportation systems for pedestrians. However, some studies examined the

behavioral characteristics of pedestrians and/or other VRUs to have a better understanding of

future alternatives [41], [42]. Similarly, some studies showed the impact of different experiences

with transportation modes on the drivers’ behavioral characteristics [39], [43]- i.e. pedestrians

with different experiences of driving, cycling, motorcycling, etc., would have a better

understanding of VRUs when they have to interact with them such as drivers who have

experiences of biking are more concerned about other bikers when are driving vehicles.

It is recommended that Virtual Training Approaches could improve the mobility of VRUs such

as elderly people and also reducing the risks associated with crossing roads [43]. Similarly, some

studies examined mobility issues of elderly and disabled pedestrians both in sharing roads with

vehicles on crosswalks and with cyclists on sidewalks [44], [45]. Some conceptual designs were

proposed, such as wearable technologies, to improve independence and confidence level of these

pedestrians in the mentioned situations [44], [46].

1 Community Road Accident Database (European)

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In terms of cognitive performance and driving behavior of elderly people, some research aims to

improve the safety issues and overcome existing barriers regarding the quality of life among

VRUs [47], [48]. Training sessions for elderly drivers such as perceptual training, eye scanning

training, and physical training are recommended to have higher confidence in driving [48].

However, the mentioned approaches could be beneficial for the existing transportation system

with manual vehicles. As technologies have been improved for using automated vehicles in

abundance, the users’ perceptions and preferences of this new technology would be altered as

well. In this regard, Sochor and Nikitas [49] showed the different perceptions and preferences of

using the same technology by two groups in the same age category [49].

New technologies have been developed most with respect to improving the safety and mobility

of drivers and car passengers rather than considering safety features for pedestrians and other

VRUs. Among limited research on new technologies for improving VRUs’ safety, Quigley et al.

[50] investigated the previous European practices to provide recommendations for the safety and

security of VRUs in Brazil and India.

In a similar study, Jacobs [51] considered the accessibility issues of disabled people in Canada

regarding transportation equality from 1976 to 2016. The results show that in many cases lack of

enough legislation support mitigates basic human rights for disabled people in terms of

transportation accessibility. For instance, in one scenario, it was found that how the mainstream

efficiency of transit systems become more important than allocating enough time and attempts

for helping disabled people to get on-board by most bus drivers. Similarly, it was observed that

disabled people feel time value and efficiency of transit systems are more valuable to serve

regular users [51].

In this regard, the new Accessibility Act in Canada has been established to improve the standards

of accessibility in five different categories: Customer Service, Employment, Information and

Communications, Transportation, and Design of Public Spaces [52], [53]. Moreover, the

Transportation Standards Development Committee in Canada provided a recommendation report

regarding the new act (Bill C-81) [52]. In general, the ideas and recommendations are in parallel

to Accessibility for Ontarians with Disabilities Act (AODA) goals [52]. According to the report,

the quiet impact of electric and autonomous vehicles has been discussed by the committee

members [52].

To achieve the best outcome, a survey was conducted among participants with/without

disabilities to share their perceptions about the new federal accessibility legislation in Canada

[54]. It is recommended that new legislation should engage different government levels

(provincial, municipal, and federal), and to ensure that all organizations are included for

providing and monitoring standards [54]. One simple solution would be building codes that can

support accessibility requirements, and participants emphasized that government funds should

support projects that will provide minimum requirements of accessibility [54].

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As a result, the main purpose of the Bill C-81 or the Accessible Canada Act is to ensure

overcoming accessibility barriers for all with a higher focus on disabled people that includes:

employment, the built environment, information and communication technologies, the

procurement of goods and services, transportation, and areas designated under some specific

regulations by legislative authority of Parliament [53]. The following sections aim to review the

ITS applications for mobility improvement of VRUs.

1.2.2 ITS application for Mobility Improvement of VRUs

Intelligent Transportation System (ITS) application has been developed for helping mobility and

safety of Vulnerable Road Users (VRUs) over the last 30 years, but it seems that industry

professionals and researchers do not achieve a universal approach for improving safety and

mobility of VRUs [55]. One reason could be related to the fast pace of technology

improvements. To minimize the mentioned gap, Mans et al. [56] investigated recommendations

on ITS applications from experts in three different categories: VRUs’ perspective, vehicles

manufacturing, and infrastructures’ role. According to the narrow-down recommendation

framework (Europe), the higher scored approaches are more expensive and require much more

time to be implemented [56]. Therefore, the fast pace of new technologies and ITS applications

should be considered by policymakers and manufacturer professionals in terms of legislation

support for VRUs before connected and autonomous vehicles become commonplace.

1.2.3 AVs/CAVs and VRUs

The traditional interaction methods, such as eye contact and hand gesture are not appropriate in

the era of AVs/CAVs. Therefore, many studies examined different alternatives for

communicating between connected and autonomous vehicles and pedestrians [12], [14], [61],

[15], [17], [18], [22], [57]–[60]. Recently, Mahadevan et al. [57] investigated the combination of

visual aids, auditory-feedbacks, and physical techniques to propose the best alternatives for

improving communication between AVs and pedestrians. It was mentioned that visual

communication aids are not appropriate for visually impaired pedestrians. Since, some

participants suggested haptic devices and audio feedback [57].

Accordingly, audible and vibration feedbacks from devices achieved a higher score among

participants [57]. Despite, both have some limitations, audible feedbacks from smartphones

would not appropriate in congested areas, as well as vibration feedback on smartphones which

would be mistaken with receiving phone calls or text messages. It should be noted that too many

cues from different devices would distract pedestrians and impact negatively for safe crossing

[57].

In general, Vehicle to Pedestrian (V2P) interaction technologies have been improved, mostly by

implementing smartphones as a major platform, combined with different technologies, such as

LED lights and WiFi connection with smartphones, etc. [15], but most studies consider visual

aids and cues which would not be practical for visually impaired pedestrians [17], [57].

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It is worth mentioning that all visual, auditory, and physically aids would be beneficial in terms

of increasing the safety of pedestrians, but some pilot studies showed that sighted pedestrians are

more rely on their perceptions about the speed of approaching vehicles and the gap acceptance

for crossing rather than feedback from devices [14], [15], [17], [57], [60].

As there is no guidelines or standards are available yet to provide universal communication

techniques between AVs and other road users, Straub and Schaefer [58] explored social aspects

and acceptance levels by the era of AVs. It seems that ‘rule of the roads’ is not sufficient to

support the safety of non-user of AVs. Therefore, policymakers should consider non-users’

behavior and acceptance regarding interaction with AVs.

Straub and Schaefer [58] aimed to provide questions for some possible interactions scenarios

between pedestrians and AVs. It is recommended that one solution would be dedicated lanes for

AVs, such as Light Rail Transit systems, but it would be complicated regarding pick-up and

drop-off passengers and also communicating with other manual vehicles on roads [58], [59].

In a similar study, Hulse et al. [59] examined pedestrians’ acceptance level both as a user of AVs

(driver/car passenger) and as a pedestrian. According to the results, most participants perceived

lower risk as a pedestrian compared to be a passenger of AVs. In addition, system failure of AVs

and having no choice for taking over driving control are two major concerns that were dedicated

by the participants [59].

In terms of being a user of connected and autonomous vehicles, some studies investigated

changes in driving behaviour and possible impacts of connected and autonomous vehicles on

mobility improvements of elderly and disabled people [62]–[64].

Robertson et al. [62] examined the possible changes in driving behaviour by introducing limited

self-driving vehicles ((LSDV) which is the level 3 definition of AV). An online survey was

conducted in Canada to investigate perceptions of LSDV. It was found that most participants

preferred to trust the LSDV with the least amount of attention on roads. According to the results,

younger drivers are less likely to watch roads in the LSDV. Similarly, as much as Vehicle Mile

Travel (VMT) increases the tendency to pay attention to roads would decrease. In terms of

preference to take other activities while using LSDV, most participants prefer to continue to

watch the roads, while this tendency is more common among women and increases by aging for

both gender [62].

Behavioral adoption by new possible users of connected and autonomous vehicles, such as older

or disabled people should be considered by policymakers and industry professionals. Therefore,

by introducing the level 5 (full-automation) of AV, Human Machine Interfaces (HMI) will play a

major role on roads [63]. As technologies have been improved, the existence of steering wheels

and other components of controlling vehicles will be replaced with HMI technologies [63].

Morgan et al. [63] examined the acceptance, trust, and likeness of connected and autonomous

vehicles among older people. The study is in parallel with the first implementation of Flourish

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project [64] in the UK with respect to HMI [63]. According to the Flourish report, the older

generation has some level of acceptance for self-driving vehicles, but they are hesitated about

full automation and giving up control of driving [64]. Morgan et al. [63] recommend some

alternatives for increasing the acceptance level of connected and autonomous vehicles by older

people. It is suggested that policymakers and designers should consider: cognitive factor (i.e.

attention, memory, perception, etc.), usability of the system in terms of simplicity, visual

impairment by aging, proper audible format in terms of complexity, and the fact that individual

preferences for using new technologies fluctuate with a higher variance among the older

generation [63].

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Literature Review

2.1 Mobility Issues of Visually Impaired People

According to the WHO, it is estimated that around “217 million people have moderate to severe

vision impairment and 37 million people are blind” globally [65]. Therefore, it is very important

to have a better understanding of possible mobility issues of visually impaired individuals in the

era of AVs.

2.2 Technology-based navigational aids for visually impaired individuals

In terms of implementing technologies for helping navigation, object detection, and orientation

of visually impaired individuals, there are two types of design: infrastructure-independent and

dependent alternatives [66]. Moreover, most of the available technologies only focus on one or

two aspects of orientation, object detection, and navigation. In general, infrastructure-dependent

technologies require higher cost and are more time-consuming [66].

One of the popular infrastructure-dependent systems is the “Beacon System” which generally

based on integrating Bluetooth pulses between a dedicated device and infrastructure sensors [66].

For instance, in the Tandem Association in Bucharest, Romania this method has implemented by

having an affordable (12-13 US$) Bluetooth Low Energy Beacon Technology which provides a

better accessibility of public transportation for visually impaired individuals [67], [68]. Besides

the affordability of device, the higher costs of infrastructure installation, modification, and

maintenance mitigate its benefits [67].

On the other hand, GPS-based alternatives are used in abundance such as “BlindSquare” mobile

application [66]. Nevertheless, GPS-based applications have also some limitations, such as non-

availability in the narrow built-up areas [66].

For improving navigation and orientation of visually impaired individuals, some studies

investigated the functionality of the current algorithms and technologies in AVs to develop

assistance navigational devices [30], [69]–[73]. In this regard, Yang et al. [70] proposed a smart

glass to provide the audio feedback for terrain awareness and prevent collisions [70].

Similarly, Lin et al. [69] investigated the functionality of current visual localization algorithms

and technologies that have used in AVs. A “Visual Localizer” was proposed for improving

navigation of visually impaired individuals which works based on “robust image matching” from

database images and query images [69].

In a similar study, Keeffe et al. [71] investigated the functionality of AVs’ technologies via long-

range lidar, to provide an obstacle detection tool for visually impaired individuals. The system is

based on the reflection of a laser pulse to an object to calculate time and convert it to the distance

information [71]. As the device is designed to be installed on a cane, the size and weight are

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limited to minimize the cumbersome for carrying. The proposed prototype was passed tests for

the 3 to 5m obstacle detection, but it is still not portable and also needs to achieve more test for

packaging requirement in different weather condition and higher distance range [71].

Likewise, Wang et al. [72] designed a wearable visual aid for improving navigation of visually

impaired individuals. The device provides auditory feedbacks (sound mapping scheme)

regarding the image information [72].

Namdar et al. [74] investigated the current United States patent for wearable devices to help

navigation of visually impaired individuals. It was found that the current definition only can

works proper on moving objects not stationary ones, and also has an operational problem in low

light conditions [74]. Since, a wireless wearable glass was proposed which uses audio

input/output transceiver for communication [74]. The system integrates GPS and specialized

satellite imagery to receives desired destinations and information wirelessly and to communicate

and inform the user about surrounding obstacles, objects, barriers, etc. [74].

More recently, Chuang et al. [75] proposed a robotic guide-dog for helping visually impaired

individuals. The proposed guide-dog is designed to overcome way-finding issues associated with

trail variations. Besides, it can save the expenditures of guide-dogs as well as costs contribute to

the infrastructure modifications [75].

Besides the above approaches, some studies focused on devices which have a higher contribution

of haptic feedbacks [76]–[78]. In this regard, Cardin et al. [78] proposed a system that uses sonar

sensors to detect an obstacle and provides vibrotactile feedback for the blind user. The system

also provides the opportunity for the user to detect dynamic obstacles due to having vibrotactile

feedback from dynamic changes [78].

In a similar study, Kassim et al. [76] proposed a new navigation aid for helping visually impaired

individuals. An audio and vibration warning feedbacks are implemented in the device which is

optional to be chosen based on the user’s preference in different situations. The system

functionality is appropriate in terms of providing enough time for decision making before

involving in an accident with obstacles, but it has some limitations such as few blind spots [76].

Another prototype based on haptic feedbacks was designed by Velázquez et al. [79]. The

assistive navigation system integrates the GPS module and the tactile-foot feedback interface to

provide navigational alerts for the visually impaired individuals [79]. Because of the importance

of sense of hearing for visually impaired individuals and the fact that most technology-based

navigational devices use auditory feedbacks, the proposed haptic system could provide

navigational feedbacks by the tactile display which could be worn on shoes and contributes with

the GPS systems on smartphones [79].

Similarly, Baseri et al. [31] proposed an affordable electrical aid for blind and deaf-blind

individuals. The proposed system helps blind and deaf-blind individuals to cross streets safely.

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The system uses a transmitter on traffic lights and a haptic receiver to provide vibration

feedbacks for the visually impaired user [31]. Despite the potential benefits of the proposed

device to be applied for AVs/CAVs, the infrastructure-dependency (sensors on traffic lights)

mitigates its benefits for using on a large-scale.

In a similar study, Khosravi et al. [30] investigated the current technologies in CAVs to design

an assistive system for improving the safety of pedestrians at signalized intersections. An

Android application was developed to provide communication among the user, the traffic

controller, the intersection infrastructure equipment unit, and the onboard unit on vehicles via

Wi-Fi. The application provides orientation information through visual, haptic, and auditory

cues. Some limitations involve in the assumptions: the user’s phone is in the direction of travel

and the smartphones’ compass is in the same direction of crossing at intersections; In addition,

the functionality of the system would not be successful unless implementation of the full

connected automated system become available [30].

Alam et al. [80] proposed another conceptual design in conjunction with the walking stick to

integrate GPS, ultrasonic sensors and a buzzer for helping blind navigation. Recently, Lu [66]

designed a “smart” glass to assess far-space spatial perceptions combined with a traditional white

cane for near-space object detection. The device receives demands from the user via a speech

application programming interface (API) [66].

Providing real-time information about public transit systems is one of the important tasks for

improving mobility and accessibility of visually impaired individuals. In this regard, Shingte and

Patil [81] provided an alert system that is specifically designed for visually impaired individuals.

It announces the detail information on buses’ route, origin and destination by using GPS and

GSM modules to send the location of users when any sudden accident occurs [81].

Similarly, Jyoti et al. [82] designed a new device which integrates GPS and GSM modules for

detecting the location of the visually impaired user. It is modified by a panic switch that alerts

the location of users to the respective person in emergency situations [82].

According to the WHO’ estimation, “most of the people with visual disability live in the low-

income situation” [81]. As most wearable technologies for helping navigation of visually

impaired individuals are relatively expensive and not available to be used on a large-scale,

Shingte and Patil [81] designed a device that works based on two main sections: the passenger

section, and the bus section. First, the user should give the desired destination through a voice

recognition system, then, the microcontroller provides bus information and announce the desired

bus number when it approaches [81].

Identifying the line and destination of public transportation vehicles, information about delays

and schedules, and overall real-time information about the current locations are the most difficult

barriers which determined by visually impaired participants in the survey [67]. Although most

participants use mobile apps for direction finding and route planning, they are not always

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accessible and participants describe their difficulties for handling devices in addition to their

other belongings [67].

2.3 Socio-economic Aspects of Transportation-Related Issues among Visually

Impaired Individuals

In terms of transportation-related issues for visually impaired individuals, some studies

investigated the impact reliability and accessibility of transportation systems on socio-economic

characteristics: such as employment, self-efficacy, stress associated with transportation, etc.

[83]–[89].

For instance, Gold and Simon [89] investigated the transportation-related issues of Canadians

with visually impairment. It was found that the majority of Canadian participants with visual

impairment are categorized in the low-income situations. The primary concern of this group was

determined to have access to information about public transportation systems [89].

In a similar study, Ball and Colette [88] showed visually impaired individuals have concerns to

be seen as a “normal” like other road users. As the majority of studies focus on orientation and

mobility improvement of visually impaired individuals by targeting different techniques,

environmental impacts, and rehabilitation programmes, Ball and Colette [88] aimed to examine

mobility decisions and its role on the level of independence of visually impaired individuals.

According to the results, one particular issue is that social views on the mobility of this group

prevent them to be seen as “normal” road users on streets, i.e. some participants acknowledged

that they avoid to use white canes or guide dogs cause it would make them be seen “abnormal”

on streets [88].

Crudden et al. [87] aimed to examine difficulties of transportation-related issues for visually

impaired individuals with a higher focus on employment issues in the US. In a similar study,

Gold [89] showed lack of proper transportation accessibilities is one of the main unmet essential

requirements that was dedicated by visually impaired participants. Likewise, Crudden et al. [87]

found that the majority of participants live near reliable public transport systems. More than one-

third of participants had to turn down a job because of transportation-related issues. It was shown

that found that rehabilitation service agencies can be more beneficial for visually impaired

individuals in finding transportation options not only for finding job opportunities, but also

improving their mobility options for other activities [87].

In a similar study, Cmar et al. [83] examined the impact of transportation self-efficacy on

employment of working-age adults with visual impairment. It is obtained that transportation self-

efficacy are more effective among younger people most for “who experienced significant vision

loss more recently”. Moreover, self-efficacy associated with transportation is highly dependent

upon age and age at onset [83].

12

In terms of difficulties for social activities, Crudden et al. [85] investigated perceptions of

visually impaired individuals about different stressful situations during walking or using public

transportation. It was found that participants have more concerns regarding three different

situations: a scenario when they try to navigate in unfamiliar places via bus routes, a scenario

when they try to walk in urban areas that do not have proper sidewalks, and a scenario when they

try to walk in unfamiliar places. Besides, employment was indicated to be the least avoided

activity because of transportation-related stress. Moreover, the participants also mentioned that

they avoid leisure activities, entertainment, and visiting families and friends due to

transportation-related stress [85].

To improve better mobility and accessibility of transportation systems for VRUs it would be

better to involve them in the design process [90]. e-Adept [91] is a Swedish service for helping to

overcome navigation barriers of VRUs with a higher focus on elderly and visually impaired

individuals. Nevertheless, the functionality of the system is proper to help any user other than

VRUs such as drivers. It also helps VRUs for indoor movements [90]. The majority of the

participants feel positive about the increasing of assurance, their independence for travel

planning and frequency, exploring new locations, and using more public transit systems instead

of Special Transportation Service (STS) which are designed for disabled people and not quite

reliable in terms of availability and accessibility. It was proposed that the universality and

affordability of their conceptual designs can be developed with legislation supports to enhance a

higher level of accessibility for the target population [90].

The majority of studies that focus on providing technologies for helping visually impaired

individuals consider the safety and security of this group in the virtual world (i.e. security of data

and personal information) [86]. Ahmed et al. [86] assessed concerns about safety and security of

visually impaired individuals in the urban environment regarding in both physical and virtual

aspects (the greater San Francisco metropolitan area). Considering public transportation as a

primary alternative for most visually impaired people, it is inevitable that they face more

challenges to find their route and also they face more threats in activities like paying for fair or

waiting at stations [86].

Overall, respondents mentioned privacy concerns: shoulder surfing, eavesdropping, concerns

associated with using ATM booth and walking without concern on the streets. Some

recommendations for coping behaviors are mentioned by participants to overcome these issues:

such as avoiding certain situations, relocation when feeling uncomfortable, mitigation

techniques, asking for help from others, avoiding to use devices outside, avoiding to share

personal information in public places, etc. [86]. According to the respondents’ preferences, they

want devices to be more discrete and not noticeable, such as something that they can attach to

their clothes [86].

The noise reduction from electric vehicles’ engines compared to traditional gasoline vehicles has

been considered a critical issue for visually impaired individuals. Mendonça et al. [92]

investigated the impact of noise reduction from vehicle operation and compared it to the different

13

pavement noise. The achievement of noise reduction seems to be beneficial with respect to noise

pollution, but it increases concerns about noise detection by VRUs (mostly elderly and visually

impaired individuals). The impact and difference of noise reduction among various type of

pavements are still not clear in terms of detecting approaching vehicles. It was found that

pavement reflection sounds are easier to detect approaching vehicles compared to the engines’

noises. However, the results were collected from simulators, therefore, in real-world conditions,

the results would be different specifically in dense urban areas [92].

Investigating level of acceptance of new technologies would be beneficial to overthinking when

new technologies such as AVs become commonplace. Accordingly, Hersh and Johnson [93]

develop a multinational survey to determine the visually impaired individuals’ preferences about

a robotic guide-dog. The survey was conducted in seven different countries and it participates

around 300 interviews with blind, visually impaired, and deaf-blind people. Overall, respondents

suggested a robotic guide should be unremarkable, easy to carry for everyday usage, and should

not be noticeable [93].

In a similar study, Brinkley et al. [33] designed an online survey to evaluate the perceptions of

visually impaired individuals about different concepts associated with self-driving vehicles. The

majority of the participants have positive views on self-driving vehicles, and most were familiar

with the technology before the survey was conducted. Besides what was found to be a primary

concern among sighted participants, Brinkley et al. [33] found that visually impaired participants

are more willing to pay extra for the technology of self-driving vehicles compared to general

sighted participants.

2.4 Research Questions

This research has two aims. First, to design a survey to understand the perception of visually

impaired individuals in relation to connected and autonomous vehicles inside and outside of

Canada. This research also aims to find potential policy recommendations for communication

techniques between connected autonomous vehicles and visually impaired pedestrians based on

the existing evidence-based recommendations and the key findings from the empirical

investigations.

14

Survey Design

3.1 Survey Design

This thesis uses a dataset that was collected through a web-based survey on individuals with

visual impairment. Survey on Visually Impaired Individuals (SUVI) was conducted by a

collaboration between the University of Toronto and the Canadian National Institute for the

Blind (CNIB). SUVI has two components: 1) Revealed Preference Survey, and 2) Stated

Adaptive Survey. Some household-level information, personal-level information and current

mode choice-related information are collected in the revealed preference section of SUVI

alongside with a wide range of socioeconomic attribute (e.g., gender, education, employment

status, auto-ownership, and severity of sight loss information were also collected).

In terms of stated-adaptive portion of SUVI, two questions are asked to assess how much

participants trust in CAVs and how much they prefer to be the users of CAVs. In the first

question, the respondents were asked to rate how much they trust in CAVs via a six-point Likert

scale ranging from ‘not at all’ to ‘entirely.' The second question, the respondents were asked

how much they prefer to be the users of CAVs via a four-point Likert scale ranging from ‘not

preferred at all’ to ‘highly preferred.' Besides, respondents were asked to select which types of

assistance or device they use while crossing the roads. As electric vehicles and possibly

connected autonomous vehicles (CAVs) run so quietly compared to the traditional gasoline-type

vehicles, some stated adaptive questions were designed to evaluate visually impaired

pedestrians’ attitudes towards the importance of hearing sound from CAVs, the low noise issue

of electric engines. A few of attitudinal questions from the SUVI is shown in Error! Reference s

ource not found..

TABLE 3-1 A few Attitudinal Questions

Questions Levels

As a pedestrian on a sidewalk, it is important to hear

vehicle noise or other kinds of noise from traffic.

Yes/No

As a pedestrian, it is important to hear warnings from

cyclists both on sidewalks and on roads.

Yes/No

As a pedestrian, it is important to hear vehicle noise

when you and a vehicle approach an intersection?

Yes/No

15

Can you distinguish the noises made by individual

motorized devices such as scooters or electric bikes on

sidewalks versus motor vehicles on roads?

Yes/No

Do you think CAVs impact your independence for

traveling?

Yes/No

As a pedestrian, how much do you trust CAVs? Not at

all/Barely/Somewhat/A

lot

Entirely/I don’t know

Consider as a passenger of a vehicle such as an Uber

or a bus that cruise without a driver. Please rate your

preference for using these CAVs vehicles in the near

future?

Not preferred at

all/Somewhat

preferred/Highly

preferred/I don’t know

Do you agree/disagree with the following statement: I

will use a navigation device which provides real-time

information (such as smartwatches, wearable cameras,

etc.) if it is not connected to other devices or a third

party.

Agree/Disagree/I don’t

know

Do you agree/disagree with the following statement: I

prefer to have the option to use a navigation device,

whether it is connected to other devices or a third

party or not. Transportation systems should support

my safety and security regardless of whether I have or

use an additional device or not.

Agree/Disagree/I don't

know.

In terms of data collection methodology, the Canadian National Institute for the Blind (CNIB)

used their contacts inside and outside of Canada by circulating SUVI through their email

subscription. Different levels of visual impairment were observed among all respondents. Since

SUVI targets different aspects of CAVs on visually impaired individuals, it was launched for

both inside and outside of Canada. A total of 352 responses were retained out of 421 complete

responses, which have all key variables for empirical investigation. The final sample has 181

respondents from Canada, and 171 from outside of Canada.

16

Figure 3-1 Comparison of the Estimated Population from CNIB and the Observed Dataset

To have a better understanding on sample distribution in Canada, Error! Reference source not f

ound. represents the comparison of the survey sample and the visually impaired population in

Canada [1]. Error! Reference source not found. shows very close matching with the visually i

mpaired population inside Canada. It can be seen that the survey sample marginally

overrepresents the visually impaired individuals in Ontario and British Columbia. Besides, the

survey sample is slightly underrepresented the visually impaired population in Quebec province.

3.2 Descriptive Statistics

A summary of descriptive statistics of the key variables that were used for empirical

investigations is shown in TABLE 3-2. Since Canadian and non-Canadian sample do not

demonstrate significant differences, all descriptive statistics are reported based on an aggregated

dataset from both Canadian and non-Canadian responses. It is found that around 55.80% of

participants have been blind or partially sighted all their life, followed by 25.80% who

experienced visual impairment between age 18 and 65 and 16.40% who experienced visual

impairment before the age of 18. Only 1.90% experienced visual impairment after the age of 65.

TABLE 3-2 Descriptive Statistics for Selected Variables

Variables Categories Percentag

e

0

10

20

30

40

50

60

Per

centa

ge(

%)

Survey Sample Distribution

Survey Sample Visually Impaired Population in Canada Reported by CNIB

17

Gender

I prefer not to say 0.90

Male 47.00

Female 51.30

Another gender identity 0.90

Education


Less than a high school diploma 2.10

High school diploma or equivalent 10.10

College of General and Vocational Education

(CEGEP)

1.20

College 19.30

Trade certificate or professional certification 6.50

Bachelor's degree 30.30

Master's degree 21.70

Doctoral or professional degree 6.50

Employment status


Employed full time 27.30

Employed part-time 11.10

Unemployed 9.70

Self-employed 6.50

Retired 26.70

Student 9.40

Unable to work 3.20

Other (please specify) 3.50

One adult 30.90

18

Household

characteristics

Two adults 43.90

One adult and a child 1.10

One adult and more than a child 2.30

Two adults and a child 4.80

Two adults and more than a child 7.60

Other (please specify) 9.30

Household vehicle

ownership

Zero 48.50

One 32.40

Two 14.40

Three 2.90

More than three 1.80

Sight loss experiences

I have been blind or partially sighted all my life 55.80

I experienced visual impairment in childhood,

before the age of 18

16.40

I experienced visual impairment between the ages

of 18 and 65

25.80

I experienced visual impairment after the age of 65 1.90

The severity of sight

loss

Mild – I have most of my vision 4.70

Moderate – I have some of my vision 20.80

Significant – I can see very little 31.20

Total – I am completely blind 36.10

I have sight loss as well as some or significant

hearing loss – I am deaf-blind

7.20

Because of the importance of the two stated-adaptive questions about how much respondents

trust in CAVs and how much they prefer to be the user of CAVs, an alluvial diagram was

19

designed to show the relation between two stated-adaptive questions (Figure 3-2). The first

question is related to the respondents’ preference for using CAVs, and the second question is

related to their level of trust in CAVs. The distribution in Figure 3-2 is sorted based on the

response in each category. Then each category is broken down by the employment status of the

respondents. It can be concluded that most participants who ‘somewhat’ trust in CAVs are not

necessarily ‘somewhat preferred’ to be the user of CAVs and vice versa. In addition, participants

who chose ‘I don’t know’ option about their level of trust in CAVs, do not necessarily choose

‘not preferred at all’ to be the user of CAVs. The results show that most participants are a full-

time employee or retired, and most participants from both groups ‘somewhat’ trust in CAVs.

Figure 3-2 Preference for Using CAVs Versus Level of Trust and Employment Status

The aggregated sample of Canadian and non-Canadian responses represents almost equal

percentages of male and female, 47.0% and 51.3% respectively. The results show that 25.3% of

respondents are over 65 years old, and 19.3% of respondents are between 55 to 65 years old. In

terms of auto-ownership, it was found that 48.5% of participants do not own a car in their

household, and 45.2% of respondents report that nobody in their households has a driver license.

The sample shows that the majority of participants (30.3%) have a bachelor’s degree, and a

master’s degree (21.7%). Besides, most participants are full-time workers (27.3%), while a fewer

portion of participants was found to be unable to work (3.2%).

20

Figure 3-3 Selected Types of Navigational Assistance or Device Respondents Use While

Crossing the Roads

The types of assistance or device that are used by the respondents while crossing the roads are

shown in Figure 3-3. Similar types of navigation assistance or device are used by both Canadian

and non-Canadian respondents (Figure 3-3). Three options are more popular, respondents are

more likely to rely on their sense of hearing, remaining eyesight, and accessible pedestrian signal

systems in sequence rather than navigational assistance while crossing the roads.

Figure 3-4 Knew about CAVs before the Survey Figure 3-5 Independence of Travelling

0 10 20 30 40 50 60 70 80 90 100

I prefer to rely on my sense of hearing when vehicles stop

before deciding when to cross

I prefer to rely on audible sounds from Accessible

Pedestrian Signals before deciding when to cross

I use my remaining eyesight to help me identify cars,

bicycles, and other pedestrians

I prefer to use technologies or apps such as eSight, Be My

Eyes, etc.

Other (please specify)

Percentage (%)

non-Canadian (171 records) Canada (181 records)

21

Figure 3-6 Preferences for Communication with CAVs

The results show that 97.1% of participants heard about CAVs before participating in the survey

(Figure 3-4). In addition, Figure 3-4 shows that 78.3% of the respondents think that CAVs

impacts their independence of travelling. In terms of communication methods with CAVs,

Figure 3-6 represents that most participants (38.5%) prefer to get feedback and alerts from

CAVs. Besides, 28.7% of participants prefer not to communicate at all, followed by 17.1% of

participants who prefer to get audible alerts from infrastructure.

Regarding the experiences with sight loss, 55.8% of respondents have been blind or partially

blind in their entire life, followed by participants who experienced visual impairment between

the ages of 18 and 65 (25.8%) and who experienced visual impairment before the age of 18

(16.4%). Only 1.9% of the respondents experienced visual impairment after the age of 65. It was

found that 84.6% of participants received Orientation & Mobility (O&M) training, which

provides hands-on training on various navigational devices for visually impaired individuals.

17.10%

38.50%

10.10%

28.70%

5.60%

How Would You Like to Communicate with CAVs When You Want to Cross

Streets?

Audible alerts from the infrastructure such as Accessible Pedestrian Signal alerts.

Alerts from the Self-driving vehicles that announce their current status, such as the Self-

driving vehicle is about to stop for you.

Alerts from wearable devices such as eSight about the Self-driving vehicle's current status,

such as the vehicle is about to stop.

I prefer not to communicate with Self-driving vehicles, their systems should be accurate in

detecting pedestrians and stopping for them.

Other (please specify)

22

Figure 3-7 Mode Choice Behavior of the Respondents

Mode choice behaviour of survey participants is shown in Figure 3-7. Cars or walk were chosen

as two major modes that visually impaired individuals use to the destination most frequently.

Besides, bicycle and paratransit are the two least used modes among visually impaired

individuals. Regarding the accessibility to the paratransit systems, the majority of Canadian

participants (44%) do not have access to paratransit compared to 28% non-Canadian participants.

Among participants who have access to paratransit, 24% of non-Canadian participants are

somewhat satisfied with the system compared to only 18% of Canadian participants.

The results show in Figure 3-9 that only 28.8% of respondents somewhat trust in CAVs. In

terms of preference for using CAVs, 26.9% visually impaired individuals highly prefer to be the

user of CAVs (Figure 3-8).

0.00%

20.00%

40.00%

60.00%

80.00%

100.00%

Car Taxi/Uber/Lyft Walk Bicycle Public Transit Paratransit

Mode Choice Behavior

Never Rarely (once monthly or less)

Sometimes (2 to 5 times per month) Frequently (2 to 5 times per week)

All the time (every day)

23

Figure 3-8 Preference for Using CAVs

Figure 3-9 Trust in CAVs

23.80%

26.90%26.90%

22.40%

How much participants prefer to be the user of CAVs?

Not preferred at all Somewhat preferred Highly preferred I don't know

15.00%

12.10%

28.80%

17.30%

6.60%

20.20%

How much participants trust CAVs

Not at all Barely Somewhat A lot Entirely I don't know

24

Empirical Model

Two econometric modeling approaches, a structural equation modelling and an ordered logit

model, were developed for the empirical investigation of this thesis. The following sections

represents a detail information of modeling approaches step by step.

4.1 SEM models:

Various mobility and communication issues of the visually impaired individuals in the context of

CAVs are captured by adopting a structural equation modelling framework in this section.

Unobserved latent constructs are determined and their relationships with the observed variables

are measured by the SEM framework. In this regard, a confirmatory factor analysis (CFA) of

responses considering the attitudinal questions is conducted to identify latent factors. The latent

factors that are constructed by using a CFA are determined by the measurement model

component written as follows:

𝑋 = 𝛽η + 𝜀 (1)

𝑌 = 𝛾𝑋 + 𝜀 (2)

Where,

𝑋 is the vector of observed independent variables,

𝑌 is the vector of dependent variables or other endogenous latent variables,

η is the vector of latent factors,

𝛾 is the coefficients of exogenous latent factors to be estimated.

In general, this modelling framework helps the evaluation of latent factors that influences

attitudinal responses from the participants in the survey and allows to draw relationships between

latent factors and the observed variables. In addition, the maximum likelihood method is used for

the model estimations. ‘safety and security’ and ‘importance of hearing’ are the two latent

factors measured to be used in the SEM model. Accordingly, two separate SEM models were

developed for Canadian and non-Canadian samples. The definition of the key variables used for

the SEM models are shown in TABLE 4-1. The final modelling structures for Canadian and

non-Canadian samples are represented in Figure 4-1 and Figure 4-2, respectively.

25

TABLE 4-1 Description of Variables for the Final Models

Variables Descriptions

Before 18 Participants who have been blind or partially sighted before the

age of 18.

Partially Blind Participants who are partially blind all their life.

Seeing AI

The smartphone application that is specifically designed for

visually impaired individuals by Microsoft. It can detect people's

faces, and objects then notify the user.

Smartphone Apps The variable that shows apps used for navigating purposes.

Google maps The variable that shows participant use Google maps for

navigation.

EV Quiet Impact Participants who believe the noise reduction of EV has negative

effects on pedestrians’ safety

Lack of

Communication

CAVs

Participants who have concerns about the lack of proper

communication techniques with CAVs.

Moral Choices

CAVs

Participants who worried about possible moral choices in the era

of CAVs.

Prefer not to

communicate Participants who prefer to not communicate with CAVs at all.

Important to hear

traffic (on-road and

sidewalk)

Participants who acknowledged that it is important to hear traffic

from on-road users as well as other users on sidewalks.

Warning from

cyclists and

motorists

Participants who prefer to get feedback and alerts from other

road users.

Heard CAV Participants who have heard about CAVs before participating in

the survey.

AV Impact

Independence

Participants who believe that AVs will impact their

independence for traveling.

Transit Frequency Participants who are frequently using public transit systems.

Car Frequency Participants who are frequently using the car.

Tech Fails CAVs Participants who have concerned about the technical failures of

CAVs.

Less College Participants who do not have a college degree.

OM Tech Use Participants who have received Orientation & Mobility (O&M)

with technology applications.

Age>65 Participants who are aged over 65.

Male Dummy variable shows a respondent is a male.

Fulltime Participants who have a fulltime job.

Trust CAV The categorical dependent variable that shows how much

participants trust CAVs.

26

Preference for

Using CAV

The categorical dependent variable that shows how much

participants prefer to be the user of CAVs.

The results show that the Root Mean Square Error of Approximation (RMSEA) is 0.145 and

0.054 for Canadian and non-Canadian samples, respectively. It is also found that A Comparative

Fit Index (CFI) are 0.332 and 0.849 for the Canadian and non-Canadian models, respectively.

The majority of coefficients are statistically significant at a 95% confidence level, and all

coefficients in the models have intuitive signs. Some non-significant variables are retained in the

models due to their behavioural importance.

As it is mentioned before, two latent factors are constructed for the Canadian and non-Canadian

models: ‘importance of hearing' and ‘safety and security.' Both latent factors impact visually

impaired individuals’ trust in CAVs and their preference to be the user of CAVs. Both latent

factors ‘importance of hearing' and ‘safety and security' produce a negative attitude towards the

level of trust and perceptions for using CAVs.

4.1.1 Communication Issues Associated with Safety and Security

The latent factor ‘safety and security’ are influenced by different variables associated with

technologies. According to the results for the Canadian model, it is shown that visually impaired

individuals who use smartphone applications for navigational purposes are less concerned about

safety and security. Similarly, the non-Canadian model demonstrates a similar pattern.

'SeeingAI' is a smartphone application that is specifically designed for visually impaired

individuals by Microsoft. It can detect people's faces and objects, and then notify the user. it is

found that 'SeeingAI' variable is statistically significant in the non-Canadian model, but not

statistically significant in the Canadian model.

As electric vehicles (EV) emit very low noise due to electric engines, it is found in the Canadian

and non-Canadian models that the noise issue of EV influences pedestrians’ safety and security.

27

Figure 4-1 SEM model result summary for Canadian sample

28

Figure 4-2 SEM model summary results for the non-Canadian sample

The models’ results show that respondents who are particularly concerned about the possible

technical failure of CAVs have positive influence on safety and security. Similarly, in both

Canadian and non-Canadian models, the technical failure positively influences the safety and

security. It is also found that the lack of proper communication techniques with CAVs positively

affects the safety and security in both models.

Due to the privacy issue, a few visually impaired individuals prefer to not communicate with CAVs

at all, which positively impact safety and security. There is an ongoing research in the literature

on ethical issues with CAVs. It is not still obvious how CAVs will make ethical decisions, i.e.

during a road crash, will a CAV try to save one bystander or the passengers in the car? The SEM

29

models show that participants who are worried about possible moral choices in the era of CAVs,

positively impact safety and security (Canadian and non-Canadian models).

4.1.2 Communication Issues Associated with Importance of Hearing

Different results are observed in Canadian and non-Canadian models regarding the

communication issues associated with hearing. The SEMs reveal that visually impaired

individuals, who are non-Canadian, proactively use mobile applications and up-to-date

technologies for navigational purposes than the Canadian visually impaired individuals. Besides,

visually impaired individuals who are using 'SeeingAI' app express less concern regarding the

importance of hearing noise from CAVs in the non-Canadian model. The non-Canadian model

also indicates individuals who take orientation and mobility (O&M) training and who use

smartphone apps for navigation are less likely to express concern in the importance of hearing

noise from CAVs.

On the other hand, 'SeeingAI' mobile application and O&M training variable are not significant

in the Canadian model. However, Google map negatively influence the importance of hearing

noise from CAVs in the Canadian model. It can be concluded that Canadian visually impaired

individuals rely more on Google Map than other navigational tools.

4.1.3 Mobility Issues Associated with Safety and Security, and the Importance of

Hearing

According to the results, some visually impaired individuals express concerns about low noise in

electric engines. This variable positively influences the safety and security in the Canadian and

non-Canadian models. It is also found that the frequency of travel by transit has a negative

influence on both latent factors: ‘safety and security’ and ‘importance of hearing.’

The survey results show that hearing traffic sounds from vehicles on roads are important for

visually impaired individuals, and they want to hear alerts and feedbacks from other road users.

This variable has a positive influence on the latent factor ‘importance of hearing’ in the Canadian

model. However, this variable is not statistically significant in the non-Canadian model. It is

found that individuals who frequently use transit are less concerned about the safety and security

of CAVs in the both Canadian and non-Canadian models.

4.1.4 Socioeconomic Attributes

All variables in the Canadian and non-Canadian models represent similar signs regarding the

socioeconomic attributes. It is found that some socioeconomic attributes influence visually

impaired individuals’ level of trust in CAVs and their preference to be the user of CAVs. For

example, the Canadian and non-Canadian models show that visually impaired individuals who

are over 65 years old are less likely to use CAVs, and they have less trust in CAVs. Male

visually impaired individuals show a positive influence on the latent factor ‘importance of

hearing’. However, they are more likely to be the user of CAVs. Besides, participants whose

30

highest degree is the high-school diploma (‘LessCollege’ in Figure 4-1 andFigure 4-2) are less

likely to use CAVs. A negative influence is shown by full-time workers regarding the level of

trust in CAVs in the Canadian model and a negative influence in terms of the preference for

using CAVs in the non-Canadian model.

In terms of sight loss experience, the only difference between the Canadian and non-Canadian

model is the ‘partially blind’ variable. In the non-Canadian model, it is found that individuals

who are partially sighted all their life are less likely to use CAVs.

4.1.5 Trust in CAV and Preference for Using CAV

Regarding the results for the two dependent categorical variables, trust in CAVs and preference

for using CAVs, the latent factor ‘safety and security’ is significant in the Canadian model. On

the other hand, ‘safety and security’ is not significant in the non-Canadian model. However, for

both Canadian and non-Canadian models the latent factor ‘importance of hearing’ is statistically

significant.

Both Canadian and non-Canadian model reveal that participants who acknowledge that it is vital

to hear traffic from on-road users as well as other users on sidewalks. In the case of preference

for using CAVs, frequency of car usage has a positive influence on the preference for using

CAVs in both Canadian and non-Canadian model. In addition, full-time workers and visually

impaired individuals whose highest degree is high-school diploma have a negative influence on

the preference for using CAVs. The variable that shows participants who acknowledged that

CAVs impact their independence of travelling has a direct positive influence on how much they

trust CAVs, which is intuitive. The following section represents another econometric modeling

approach that was developed for the purpose of this thesis.

4.2 Ordered Logit models:

This section aims to determine the critical factors that affect the level of trust in CAVs as well as

preference for using them among the visually impaired pedestrians by integrating an Ordered

Logit modelling approach. Since both levels of trust in CAVs and preference for using CAVs are

ordered categorical attributes, it is warranted to choose a mathematical model which can

seamlessly handle multiple categories and account for the ordering. Thus, an ordered probit or an

ordered logit model can be employed in this context. In terms of the error term, the probit model

assumes a normal distribution and logit model assumes logistic distribution. Since Ordered Logit

has a closed-form formulation of the probability equations, ordered logit model is selected for

the econometric modelling framework in this study.

The probability of rank j being chosen is shown by Equation (3). The thresholds are shown by 𝜏𝑖 that depends on the levels of dependent categorical variables. In the final model specification, all

parameters are selected based on a 95% confidence interval. Some of the variables with low t-

value and proper signs are kept in the final model since provide important policy

recommendations.

31

𝑌𝑖∗ = ∑𝛽𝑖 𝑥𝑖 + 𝜀𝑖 (1)

𝑌𝑖 =

{

1 𝑖𝑓 − ∞ ≤ 𝑍ℎ

∗ < 𝜏1

2 𝑖𝑓 𝜏1 ≤ 𝑍ℎ∗ < 𝜏2

3 𝑖𝑓 𝜏2 ≤ 𝑍ℎ∗ < 𝜏3

4 𝑖𝑓 𝜏3 ≤ 𝑍ℎ∗ < +∞

}

(2)

𝑃(𝑌𝑖 = 𝑗|𝑋𝑖) =𝑒𝑥𝑝[𝜇(𝜏𝑗−𝛽𝑥𝑖)]

1+𝑒𝑥𝑝[𝜇(𝜏𝑗−𝛽𝑥𝑖)]−

𝑒𝑥𝑝 [𝜇(𝜏𝑗−1−𝛽𝑥𝑖)]

1+𝑒𝑥𝑝 [𝜇(𝜏𝑗−1−𝛽𝑥𝑖)] (3)

The definition of the key variables that are used for the empirical investigation in the ordered

logit models are shown in TABLE 4-2.

TABLE 4-2 Description of the Key Variables

Parameters Descriptions

Totally Blind Participants who have been blind or partially sighted all their life.

Severe Total Blind Participants who are totally blind all their life.

All Life The variable that shows a respondent is blind or partially sighted all her life.

Non-App Navigation The variable that presents non-tech devices for navigating purposes.

App Navigation The variable that shows apps and tech-devices used for navigating purposes.

Alert CAVs Participants who prefer to get feedback or alerts from CAVs.

Devices (no 3P) Participants who prefer to use devices for communication that are not

connected to a third party.

EV Quiet Impact Participants who believe the noise reduction of EV has adverse effects on

pedestrians’ safety

32

Lack of

Communication

Participants who have concerns about the lack of proper communication

techniques with CAVs.

Moral Choices Participants who worried about possible moral choices in the era of CAVs.

Transit Frequency Participants who are frequently using public transit systems.

Transit Access Rate Participants who are somewhat satisfied regarding the available public

transit.

Para-transit Access

Rate

Participants who are somewhat satisfied regarding the available paratransit

systems.

Car Frequency Participants who are frequently using the car.

HH # vehicles The number of vehicles in a household.

Tech Fail Concern Participants who have concerns about the technical failures of CAVs.

Less College Participants who do not have a college degree.

Accident EV Participants who had the experience to be near an accident with an EV.

Alert Infra Participants who prefer to get feedback or alerts from infrastructures in

terms of communication with CAVs.

OM Tech Use Participants who have received Orientation & Mobility (O&M) with

technology applications.

Age<45 Participants who are aged below 45.

Age>65 Participants who are aged over 65.

Female Dummy variable shows a respondent is a female.

4.2.1 The Level of Trust in CAVs

The results of the ordered logit model for the level of trust in CAVs are shown in TABLE 4-3.

This model captures the visually impaired respondents' trust in CAVs. The Canadian and non-

Canadian sample were used separately to estimate two models for each sample. Visually

impaired respondents are asked to rate their level of trust in CAVs using a six-point Likert scale

ranging from ‘not at all’ to ‘entirely.’ For the simplicity of the model the six levels are

transformed to the five levels as follows: ‘not at all,’ ‘barely,’ ‘somewhat,’ ‘a lot,’ and ‘entirely.’

TABLE 4-3 also reports the marginal effects derived from the level of trust in the CAVs model.

33

The marginal effect provides the percent change in the alternative’s probability due to a unit

change in the explanatory variable.

The level of trust in the CAVs model (TABLE 4-3) shows that Canadian participants who are

satisfied with the public transit accessibility tend to trust in CAVs. In contrary, the non-Canadian

respondents who frequently use public transit systems are less likely to trust in CAVs. Model

results reveal that Canadian participants who rely on mobile applications and technology-based

devices for navigation purposes are more likely to trust in CAVs. The marginal effect shows that

the visually impaired individuals who use up-to-date technology for navigational purposes tend

to trust in CAVs (‘somewhat trust,’ ‘a lot’ and ‘entirely’). The non-Canadian model indicates

that visually impaired individuals who acknowledge their concerns about the technical failures of

CAVs are less likely to trust in CAVs. Besides, the non-Canadian model also shows that visually

impaired individuals who prefer to get feedback or alerts from infrastructures in terms of

communication with CAVs are less likely to trust in CAVs.

Both Canadian and non-Canadian models reveal that participants who experienced to be near an

accident with an electric vehicle (EV) are less likely to choose CAVs. Model results indicate that

Canadian participants who have concerns about the lack of proper communication techniques

with CAVs are less likely to trust in CAVs. The results show a debate on ethical issues with

CAVs among the respondents. For example, during an accident, will an automated vehicle try to

save one innocent bystander or the passengers in the car? Canadian participants who believe

moral choices of CAVs will be an issue are less likely to trust in CAVs.

The results in the Canadian model show that participants who have been congenitally blind are

less likely to trust in CAVs. However, non-Canadian participants who are blind or partially

sighted all their life tend to trust in CAVs. It is found in the Canadian and non-Canadian model

female participants tend not to trust in CAVs. Both Canadian and non-Canadian models

demonstrate that visually impaired individuals whose highest degree is a high-school diploma

(do not have a college degree) are less likely to trust in CAVs. Participants who are aged over 65

are less likely to trust in CAVs in both Canadian and non-Canadian model. The Canadian Model

shows that Canadian participants who have a higher number of vehicles in their households are

more likely to trust in CAVs.

TABLE 4-3 Empirical Model for the Level of Trust in CAVs

The level of trust in CAVs (Canadian Sample)

Parameter estimations The marginal effects

Parameters Value t-value Not at

all

Barely Somewha

t

A lot Entirely

Transit Access

Rate

0.29 2.24 -0.03 -0.034 0.01 0.042 0.012

34

Age>65 -0.8 -1.15 0.106 0.084 -0.069 -0.097 -0.024

Lack of

Communication

-0.85 -1.93 0.075 0.094 0 -0.128 -0.041

Moral Choices -1.88 -4.19 0.15 0.186 0.057 -0.274 -0.118

App Navigation 0.98 2.55 -0.108 -0.107 0.045 0.133 0.037

Severe Total Blind -0.72 -1.94 0.071 0.081 -0.016 -0.105 -0.031

Female -1.42 -3.72 0.165 0.148 -0.072 -0.187 -0.053

HH # vehicles 0.58 2.49 -0.06 -0.067 0.02 0.083 0.023

Less College -0.94 -2.51 0.103 0.103 -0.042 -0.129 -0.036

Accident EV -1.05 -2.05 0.139 0.107 -0.088 -0.126 -0.031

𝜏1 -3.32 -3.93

𝜏2 -2.04 -2.49

𝜏3 -0.14 -0.17

𝜏4 1.85 2.22

The level of trust in CAVs (Non-Canadian Sample)


Parameters Value t-value Not at

all

Barely Somewhat A lot Entirely

Totally Blind 0.83 1.80 -0.088 -0.069 0.001 0.138 0.018

Tech Fail Concern -1.65 -3.42 0.151 0.124 0.06 -0.287 -0.048

Transit Frequency -0.25 -1.72 0.031 0.022 -0.01 -0.038 -0.004

Age>65 -1.19 -2.44 0.146 0.103 -0.049 -0.179 -0.021

Alert Infra -0.63 -1.24 0.09 0.052 -0.049 -0.084 -0.009

Accident EV -1.45 -3.08 0.227 0.101 -0.133 -0.176 -0.019

Female -0.65 -1.68 0.076 0.055 -0.017 -0.102 -0.012

Less College -1.09 -2.42 0.158 0.085 -0.085 -0.142 -0.016

35

𝜏1 -9.37 -4.32

𝜏2 -8.44 -3.95

𝜏3 -6.27 -3.02

𝜏4 -3.55 -1.74

4.2.2 The Preference for Using CAVs

TABLE 4-4 shows the Ordered Logit model results, which capture the visually impaired

respondents’ preference for using CAVs. Similar to the level of trust in CAVs, two separate

models are estimated for Canadian and non-Canadian sample for the preference for using the

CAVs. A four-point Likert scale ranging from ‘not preferred at all’ to ‘highly preferred’ was

given to the visually impaired respondents to rate their preferences for using CAVs. For the

simplicity of the model, the four levels are transformed to the three levels as follows: ‘not

preferred at all,' ‘somewhat preferred,' and ‘highly preferred.’ The marginal effects derived from

the preference for using CAVs are also shown in TABLE 4-4.

One finding is related to the previous experience to be near an accident with an electric vehicle

(EV). Participants involved in an experience to be near an accident with EV are less likely to use

CAVs in the Canadian and non-Canadian model. Similarly, visually impaired individuals, who

express concerns about the low-noise issue of EV, do not prefer using CAVs. Canadian model

shows that participants who prefer to get feedback or alerts from CAVs are more likely to use

CAVs. Canadian participants who acknowledged that they rely on conventional navigation tools,

such as a white cane or a guide dog, are less likely to use CAVs. The Ordered Logit model

indicates that Canadian respondents, who prefer to use devices for communication that are not

connected to a third party, are more likely to use CAVs.

In the Canadian model, it is shown that participants who frequently use car or public transit

system are more likely to use CAVs. On the other hand, in the non-Canadian model, these

variables are not significant. Since, the frequency of car and public transit usage variables are

excluded from the non-Canadian model. The Canadian model depicts that participants who are

somewhat satisfied regarding the available public transit do not prefer to use CAVs. The non-

Canadian model results show that participants who are satisfied regarding the available

paratransit systems are less likely to explore new modes such as CAVs.

The Canadian model demonstrates that participants who have been blind or partially sighted all

their life are less likely to use CAVs. Similarly, the non-Canadian model indicates that if a

respondent is blind or partially sighted for the entire life, they are less likely to use CAVs. It is

found in the both Canadian and non-Canadian model that visually impaired individuals, whose

highest degree is a high-school diploma (do not have a college degree), are less likely to use

CAVs. Gender effect is visible in the non-Canadian model as the female participants tend not to

prefer using CAVs. It should be notified that age variable is used as a continuous variable in the

36

non-Canadian model. The non-Canadian model depicts that older individuals who are visually

impaired do not prefer using CAVs. The following chapter represents the existing policy

recommendations regarding the possible solutions for communication with CAVs, and the key

finding from this thesis empirical investigation for future research.

TABLE 4-4 Empirical Model for Preference for using CAVs

Preference for using CAVs (Canadian Sample)


Parameters Value t-value Not preferred at all Somewhat

preferred

Highly

preferred

Totally Blind -0.38 -0.88 0.079 0.001 -0.080

Transit Access

Rate

-0.25 -1.74 0.050 0.003 -0.052

Non-App

Navigation

-0.63 -1.41 0.133 -0.006 -0.127

Alert CAVs 0.62 1.55 -0.120 -0.015 0.135

Devices (no 3P) 0.99 2.54 -0.196 -0.015 0.211

EV Quiet Impact -1.04 -1.55 0.171 0.074 -0.245

Transit Frequency 0.33 2.09 -0.066 -0.004 0.069

Car Frequency 0.57 2.92 -0.115 -0.006 0.121

Age<45 -1.28 -2.08 0.297 -0.086 -0.211

Less College -0.86 -2.11 0.178 -0.002 -0.176

Male 0.7 1.76 -0.143 -0.001 0.144

Accident EV -0.71 -1.28 0.155 -0.020 -0.135

𝜏1 0.11 0.09

𝜏2 1.87 1.44

Preference for using CAVs (Non-Canadian Sample)

37


Parameters Value t-value Not preferred at all Somewhat

preferred

Highly

preferred

All Life -0.05 -0.12 0.009 0.002 -0.01

Less College -0.81 -1.68 0.163 -0.006 -0.157

Female -1 -2.53 0.173 0.044 -0.218

Age (continuous

variable)

-1.21 -2.08 0.22 0.039 -0.259

Accident EV -1.11 -2.49 0.224 -0.014 -0.21

Para-transit Access

Rate

-0.26 -2.56 0.048 0.009 -0.056

OM Tech Use -0.95 -2.14 0.186 0.002 -0.188

𝜏1 -8.2 -3.3

𝜏2 -6.2 -2.6

38

Policy Recommendations

5.1 Introduction

Drastic improvements in the technological advances in the automobile industry and

transportation systems has been made, which emerge towards the connected autonomous

vehicles (CAVs). Different aspects of CAVs have been investigated through a series of previous

studies, which put emphasize on the level of acceptance, safety, security, congestion mitigation,

the environmental benefit of the CAVs [6]–[9]. Despite the fact that a considerable amount of

research is done on different aspects of connected and autonomous vehicles in the past, majority

of the studies focus mainly on passengers’ perceptions and attitudes towards connected and

autonomous vehicles [6]–[9]. There is a limited research about how connected and autonomous

vehicles will communicate with non-motorized road users such as pedestrians when different

levels of automation [15], [17], [22].

Developing a viable communication technique between pedestrians and autonomous vehicles has

been examined by some manufacturers [24]–[29]. For instance, focusing on Light-emitting diode

(LED)-enhanced warning alerts where LED lights are placed in front of the vehicles was

investigated by a few studies [15]. An autonomous vehicle can communicate with roadside

pedestrians and other non-motorized road users by intergrating LED-enhanced technology.

Though, this communication techniques are not applicable for visually impaired pedestrians.

Despite, there is only a few studies that focused on the possible communication techniques

between visually impaired pedestrians and traffic management systems that also can be applied

for communicating with autonomous vehicles [30], [31], there is no evidence that industry

professionals or policy makers consider the impacts of CAVs on visually impaired pedestrians

and how they want to communicate with these technologies. As an illustration, a GPS-based

communication system that provides connection between the pedestrians’ smartphones and the

connected and autonomous vehicles designed by Khosravi et al. [30]. A communication system

between pedestrians and the traffic management system is also provided by the proposed system.

Moreover, in the existing literature, only a few studies explored the perceptions of visually

impaired people on autonomous vehicles [32], [33]. It found that the visually impaired

participants believed they were not considered in the processes of technology development of

autonomous vehicles. It was also observed that most visually impaired participants have positive

views on the potential benefits of autonomous vehicles. Meanwhile, they also have concerns

regarding the possible issues such as technology failures of these vehicles [32], [33].

The remainder of this section is structured as follows: Section 5.2 represents a review of the

literature on common practices of communication techniques in the context of CAVs; Section

5.3 matching the key findings from the SEM and the Ordered Logit models with policy

recommendations and some possible future research ideas.

39

5.2 Background

The focus of the existing literature is on providing visual signals to sighted pedestrians and

cyclists. Since, existing literature lacks how assistive interfaces will be developed and integrated

with CAVs for visually impaired pedestrians. Different countries have proposed policy

recommendations and guidelines about how to embrace the changes due to emerging CAVs

technologies [22], [24], [25], [27], [29]. Regarding the literature and country-specific guidelines,

the following classifications are developed for the communication technologies of CAVs with

visually impaired individuals:

5.2.1 Smartphones as the Core of Communication Systems

It is recommended that audible and visual alerts can be a practical solution for overcoming

communication issues with autonomous vehicles [25]. It is also predicted that wireless

connection system, which includes the cellular mobile communications and WiFi connection

systems, can be considered as the core of such communication systems of the CAVs. Many

studies have been recommended to use a smartphone as a communication platform among

pedestrians, cyclists, and autonomous vehicles [25], [94]. However, it would be better to have

communication between smartphones and autonomous vehicles as a complementary approach,

and it should not overtake the responsibility of communication with CAVs to the pedestrians or

other VRUs. It is inevitable that wireless connectivity would be established between autonomous

vehicles, conventional motorized vehicles, non-motorized vehicles, infrastructures and

pedestrians [25]. Khosravi et al. [30] proposed a system that uses an android application to

improve the accuracy of the GPS module for pedestrians. It also provides communication signals

from a traffic controller and autonomous vehicles via wireless connection [30].

5.2.2 Vehicle-to-vehicle (V2V) and Vehicle-to-infrastructure (V2I)

There are typically two connection methods adopted in CAVs technology. Cellular network

would be the first method to provide consumer conveniences and infotainment from vehicles

connected to the internet. Vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) are

considered as the second method in terms of communication [27]. This would happen over

Dedicated Short-range Communication (DSRC) systems [27]. There is an ongoing discussion on

whether DSRC or 5G cellular would perform better for connected, and autonomous vehicles.

Some studies depicts that DSRC will be the base platform for short-range communication and

5G cellular networks will be a practical application for a wide range of coverage [27].

5.2.3 LED-enhanced Technologies

One of the promising alternative for communication between pedestrians and autonomous

vehicles is Semcon’s concept, which is called ‘smiling car’ [28]. The system utilizes the LED-

enhanced visual display, which provides stopping or passing signals to pedestrians by providing

‘smile’ or ‘stop’ sign [28]. Another assistive alternative would be windshields that also can be

40

used as screens for providing navigation guidance for pedestrians and cyclists [23]. For instance,

windshields can simulate driver’s behaviours such as hand gestures and eye contact. These visual

assistive alternatives can be modified by adding auditory signals for assisting visually impaired

individuals.

5.2.4 Auditory Feedback

Regarding the existing auditory feedback prototypes, a recent study designs to provide audible

messages by speakers about the current status of autonomous vehicles [95]. Besides, auditory

feedback would help both sighted and visually impaired pedestrians [96]. A system which

integrates low-cost electronic devices with traffic lights is proposed by Baseri et al. [31]. The

proposed system helps visually impaired and deaf individuals by providing real-time information

about nearby traffic lights locations and its status [31].

5.2.5 Safety Zone

An alternative solution is recommended by Gibson [24], which will enhance the overall safety to

the pedestrians. The concept of ‘safety zone’ will minimize possible conflicts with autonomous

vehicles by providing a separate infrastructure for pedestrians [24]. A higher level of safety

would be achieved for sighted pedestrians by using the concept of ‘safety zone’. However, to

achieve the best outcome for all pedestrians, some modifications will require to provide an equal

level of safety for visually impaired pedestrians.

Alongside with the ‘safety zone’ concept, on-street parking would have potential benefits, which

is considered by some studies [25], [27], [97], [98]. Since, the current land-use for on-street

parking would be less required in the era of autonomous vehicle, it would be used for other

purposes than housing, such as separate bike paths or separate sidewalks for pedestrians [27].

5.2.6 Artificial Intelligence

Since Artificial Intelligence (AI) will gradually replace human drivers, detecting vehicles,

pedestrians, cyclists, and infrastructures is an ongoing research. Thus, technologies for detecting

different types of pedestrians, such as detecting white canes in case of visually impaired

pedestrians will be required. Currently Radar, LIDAR, and DSRC are commonly used to

distinguish obstacles, pedestrians, cyclists, and other vehicles. In this regard, many

manufacturers have been working on a combination of different techniques to overcome

detection issues and provide interfaces and displays for better communication of connected and

autonomous vehicles with other road users [20], [29].

41

It is clear from the abovementioned discussion that most studies focus on drivers and their

acceptance to become the users of autonomous vehicles. Therefore, more research and

simulations are required to examine the interaction between pedestrians and autonomous

vehicles [20], [96], [99]. As a limited number of studies considered the impact of autonomous

vehicles on visually impaired pedestrians [30], [32], [33], this thesis targets the latest

recommendations on communication techniques between CAVs and pedestrians to provide a

policy framework to be used for further investigation. The recommendations are based on the

key findings from the econometric modeling results, the SEM model and the ordered logit

model, which were developed on a dataset from a survey on visually impaired pedestrians.

TABLE 5-1 represents a summary of existing literature on communication techniques between

pedestrians and connected/autonomous vehicles.

TABLE 5-1 Summary of Existing Literature on Communication Techniques with CAVs

Recommendations for Communication Techniques between Pedestrians and AV/CAVs

References Recommendations Limitations

Gibson [24];

Automated vehicles: Do we

know which road to take?

[25];

The Future of Automated

Vehicles in Canada Report

of the PPSC Working

Group on Connected and

Automated Vehicles. [26]

Integrated framework with

WiFi, Cellular network, and

Smartphones

• Expensive

• WiFi and smartphones are mandatory

ITS Asia Pacific Forum in

Fukuoka [29]

Communication between CAVs

and pedestrians’ through

smartphones.

• Smartphone needs to be carried all the

time

Semcon [28] Displays for showing the current

status of vehicles • This tool is not ready for visually

impaired individuals.

• WiFi is mandatory

Mirnig et al. [23] • Vehicles’ wind shields as a

simulator of real drivers’

characteristics, such as hand

gestures, eye contact

• This tool is not ready for visually

impaired individuals.

He et al. [13] • Combination of Bluetooth

and DSRC


time

42

• WiFi is not necessary

Policy Recommendations for Mitigating the Risk of Incidents between Visually Impaired Pedestrians

and CAVs

Advantages Limitations

Baseri et al. [31] • A low-cost device

• Precise info about traffic

signals, i.e. time to cross the

road

• Specially designed for blind

and deaf-blind people

• Short range of the detection system

• Size of the designed devices are

bigger than smartphones

• Infrastructural modification is

required

Khosravi et al. [30] • Integrates CAVs, android

system and GPS module for

visually impaired

pedestrians

• Provides communication

signals to the pedestrians.

• This device should be set in the exact

direction of travelling


required


time

Gibson [24] • Separate infrastructure for

pedestrians

• Modifications required depending on

the type of disabilities.

A Summary of Possible Policy Recommendations for Communication Techniques between Visually

Impaired Pedestrians and CAVs

Advantages Limitations

New electric vehicles (also

CAVs) should have an

acoustic system when

running below 19km/h. This

is a new rule which is

enforced by European

Union [2].

• Increasing the safety

perception levels about

CAVs

• Cost effectiveness compared

to modifying infrastructure

• Increasing the risk of noise pollution

• Challenging in dense urban areas with

higher volume of traffic

Grade separated crosswalks

(overpass/underpass) [100] • Beneficial in dense urban

areas


expensive.

• Problematic in terms of navigation for

visually impaired pedestrians

43

• Practical for both sighted

and visually impaired

pedestrians

Developing affordable

wearable devices to be used

on a large-scale, such as

smart glasses (virtual eyes

for visually impaired

pedestrians), and smart

watches, etc.

• These devices will integrate

users, traffic control

management systems, and

traffic lights.

• Asking visually impaired pedestrians

to handle more devices for their own

safety could not be a good solution.

• This practical approach could be

beneficial for navigation of this

community; however, it shifts higher

levels of responsibility from

transportation service providers to the

users.

Alerts from CAVs for their

current status: about to stop,

give a right of way to the

pedestrians, on the full-

automation mode. [15]

• Proposed by the different

manufacturers such as Benz

and Mitsubishi

• Focused on visual aids only such as,

LED lights.

• Acoustic systems should be added to

the system.

5.3 Matching Key Finding from the SEM and the Ordered Logit Models with

Policy Recommendations

According to the results from the survey and the econometric models’ results, it was observed

that integrating technologies in Orientation & Mobility (O&M) training sessions has a positive

impact on perceptions about how much participants trust CAVs and how much they prefer to be

the user of CAVs. As a result, one policy recommendation could be providing additional

subsidies by governments for advocacy groups such as CNIB to provide O&M programs that

integrate technologies for navigation purposes.

Another key finding from the SEM model is related to the latent ‘importance of hearing’ factor.

It was found that participants are more concerned about the quiet impact of electric engines in

electric and autonomous vehicles. As a result, one possible recommendation would be a

combination of communication systems via pedestrians’ smartphones, vehicles, and

infrastructures to provide additional audible feedback in cases that pedestrians and CAVs would

have conflicts, such as intersections. An acoustic alert system from CAVs would be a good

solution for visually impaired pedestrians as well as distracted pedestrians. Law enforcements

would be helpful to achieve this goal which has been done in EU recently. As all new EV

vehicles should have an acoustic alert system while running below 19 km/h [2].

In addition, it is inevitable that a communication system should be designed for pedestrians and

CAVs. This can be provided by an application on smartphones or it can be an alert system such

44

as the Accessible Pedestrian Signal (APS) system, or a combination of different alternatives.

However, any possible solutions for this matter should not overtake the responsibility of any

accidents or collisions to pedestrians because they are not carrying their devices or not using the

system correctly. All possible solutions should be designed in a way to provide additional trust

and safety for pedestrians in any kind to feel safe why crossing streets or walking on sidewalks.

There are a lot of studies and reports that mentioned the communication between vehicles,

infrastructure, and other road users in the era of autonomous vehicles would be possible via

DSRC or 5G technologies. Again, upgrading the mentioned communication systems by adding

auditory feedback would be an asset to provide accessibility and safety for all pedestrians.

To sum up, recommendations that are more focused on smartphones’ application or minor

infrastructure modifications would save governments a lot of budget rather than thinking about

changing the whole infrastructure for the era of CAVs. However, this kind of modification would

not happen over night, therefore, slight changes such as a system that connects to people’s

smartphones, traffic lights, and traffic managements [30], [31] would be a good solution to

prevent thinking again when autonomous vehicles become commonplace. It seems that this kind

of recommendation could save a lot of money for government and are more realistic compared to

policies that are more focused on changing the whole infrastructures for the purpose of CAVs.

However, it is worth noting that this recommendation does not admit that the responsibility of

communication with connected and autonomous vehicles in any sorts should be turned over to

pedestrians (more importantly pedestrians with disabilities (such as visual impairment)), but it

says that pedestrians or other non-motorized road users in the era of autonomous vehicles feel

safer while commuting because connected and autonomous vehicles are designed to detect other

road users rather they want to communicate with these vehicles or not.

45

Conclusion

This thesis provides useful insights for understanding the perceptions about CAVs among

visually impaired individuals. This thesis represents two econometric models to capture the

possible mobility issues of visually impaired pedestrians in the era of connected autonomous

vehicles. The thesis used the dataset that was collected through a survey among visually

impaired pedestrians by a collaboration between the University of Toronto and the Canadian

National Institute for the Blind (CNIB). The dataset includes the current mobility issues of the

target population, perceptions on electric, autonomous, and connected autonomous vehicles, how

much participants trust CAVs and prefer to be the user of CAVs, alongside some questions about

socio-economic characteristics. A series of econometric models are estimated to uncover the

critical factors that affect the level of trust in CAVs and the preference for using CAVs among

visually impaired individuals. The latent factors, ‘safety and security’ and ‘importance of

hearing’, were generated through a confirmatory factor analysis embedded in a structural

equation model.

A customized web-based survey was designed, which incorporated both revealed preference and

stated-adaptive questions. The survey sample represents visually impaired individuals from both

Canada and outside of Canada. The survey reported participants opinions on how much they

trust CAVs as a set of choices (not at all, barely, somewhat, a lot, entirely, and I don’t know).

Similarly, it reported how much participants are preferred to be the user of CAVs as a set of

choices (not preferred at all, somewhat preferred, and highly preferred).

The results from the SEM model show that two latent factors, ‘safety and security’ and

‘importance of hearing’, both have negative influences on how much participants trust and prefer

to be the user of CAVs. Empirical results also show that technology failures of CAVs and

conflicts situations when CAVs have to decide to collide with a pedestrian or other vehicles or

obstacles are the most effective parameters that influence on the latent ‘safety and security’

factor. Frequency of traveling by public transit systems is another influential parameter that

affects two latent factors negatively. In other words, participants who acknowledged that they

travel more frequently by public transit systems have higher preference for using CAV and they

seem to have higher level of trust to CAVs. Moreover, lack of proper communication techniques

with CAVs and the quiet impact of electric engines play a major role directly and indirectly via

latent factors on how much participants trust CAVs and how much they prefer to be the user of

CAVs. On the other hand, it was observed that parameters that show participants rely on using

smartphones’ applications for navigation purposes, such as Google Map, have opposite

influences on latent factors and show better perceptions of participants regarding how much they

trust CAVs and how much they prefer to be the user of CAVs.

One key finding from the ordered logit model is the importance of hearing sounds from CAVs on

roads and cyclists on sidewalks. The majority of the participants prefer to get feedback and alerts

from CAVs. Canadian participants who experienced to be near an accident with an electric

46

vehicle (EV) are less likely to choose CAVs. The ordered logit model results show that Canadian

participants who have concerns about the lack of proper communication techniques with CAVs

are less likely to trust in CAVs. The ordered logit model results also reveal that Canadian

participants who rely on mobile applications and technology-based devices navigating purposes

tend to trust in CAVs. Ethical issues with CAVs are a point of contention among the

respondents. The level of trust in CAVs model shows that Canadian participants who are

satisfied with the public transit accessibility are more likely to trust in CAVs. In contrary, the

non-Canadian respondents who are frequently using public transit systems are less likely to trust

in CAVs. Canadian participants who are totally blind in their entire life are less likely to trust in

CAVs. However, non-Canadian participants who are totally blind in their entire life are more

likely to trust CAVs. The model results found that the level of education is a determining factor

towards the perception of CAVs.

In terms of preference to use CAVs, both ordered logit models for the Canadian and non-

Canadian sample demonstrate a similar trend. The preference to use CAVs model shows that

Canadian participants who acknowledged that they rely on conventional navigation tools, such as

a white cane or a guide dog, are less likely to be the users of CAVs. On the other hand, it is

found that Canadian participants, who are more frequently use car or public transit system, are

more likely to use CAVs. The model results show that secured communication with CAVs is a

prerequisite for visually impaired individuals to use CAVs.

As a result, we would like to propose two major policy recommendations regarding the

communication techniques between visually impaired pedestrians and CAVs. On one hand, we

can say that with the great support from the econometric model’s findings, the quiet impact of

electric engines is dedicated to being one of major issues. In this regard, we would like to

mention the great opportunity that smartphone’ applications are able to provide better mobility

and navigation improvement. For instance, applications such as a conceptual design by Khosravi

et al. [30] which are based on a communication system among pedestrians, vehicles, and

transport management system are able to fix the communication issues for visually impaired

pedestrians and are cable of providing higher level of safety for all types of pedestrians.

Accordingly, one recommendation would be providing enough subsidies for developers to

specifically design apps for visually impaired pedestrians.

On the other hand, government can provide subsidies for advocacy groups such as CNIB in the

purpose of providing Orientation & Mobility (O&M) training programs that integrate

technologies for navigation purposes. In addition, technologies and connectivity approaches,

such as DSRC and 5G systems are supposed to have some sort of connections with other road

users’ smart devices such as smartphones. Since, another solution can be modifying these

systems to be more accessible by having audible feedbacks and alerts for visually impaired

pedestrians.

Lastly, law enforcements would be an asset to improve the accessibility of transportation

systems. According to the results from the econometric model in this paper, it was observed that

47

hearing alerts and feedbacks from vehicles on roads and cyclists on sidewalks are important

parameters that should be considered by policy makers. In this regard, new rules such as one that

recently became mandatory in EU would be practical. The new rule states that all new electric

vehicles in EU should have an acoustic alert system while they run in speed lower than 19 km/h

to achieve a higher level of safety for visually impaired pedestrians [2]. It is mentioned that by

2021, all EVs should be equipped with the system [2].

48

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57

Appendix: Survey on Visually Impaired Individuals (SUVI)

Understanding the impact of connected and automated vehicles for pedestrians with sight loss

________________________________________

Informed Consent

1) By clicking "Yes I agree to participate in this survey" below, I acknowledge that I understand

the purpose of the survey, and how my responses to the survey questions will be managed,

analyzed, and used by the researchers.

( ) Yes I agree to participate in this survey

( ) No, I do not agree to participate in this survey

________________________________________

Your Location

2) Your Location:

What country do you live in?: _________________________________________________

In which City/Town/Village are you living?:

_________________________________________________

What is your ZIP/Postal code?: _________________________________________________

________________________________________

Your Experience with Sight Loss

3) Which of the following statements best describes you?

58

( ) I have been blind or partially sighted all my life

( ) I experienced visual impairment in childhood, before the age of 18

( ) I experienced visual impairment between the ages of 18 and 65

( ) I experienced visual impairment after the age of 65

4) In your opinion, which of the following best describes the severity of your sight loss?

( ) Mild – I have most of my vision

( ) Moderate – I have some of my vision

( ) Significant – I can see very little

( ) Total – I am completely blind

( ) I have sight loss as well as some or significant hearing loss – I am deafblind

________________________________________

How You Get Around

5) Have you received orientation & mobility training?

( ) Yes

( ) No

( ) No, but waiting for service

( ) I prefer not to say

6) Has this orientation & mobility integrated technology use for the purpose of navigational aids?

( ) Yes

( ) No

59


________________________________________

How You Use Technology

7) Which of the following technologies, aids, and services, if any, do you use on daily basis.

Please check all that apply.

[ ] Screen reading programs such as JAWS, NVDA or Voice Over for Mac

[ ] Screen magnifying programs such as ZoomText

[ ] Large monitor

[ ] Custom computer/desk workstation

[ ] iPhone

[ ] iPad

[ ] A smartphone other than an iPhone

[ ] A tablet other than an iPad

[ ] Reader apps such as KNFB Reader or Seeing AI

[ ] Be My Eyes

[ ] AIRA

[ ] A CCTV or closed-circuit television

[ ] A DAISY or Digital Accessible Information System

[ ] Audio book player

[ ] A refreshable braille display device

[ ] A talking watch

[ ] Other talking products such as a kitchen timer or alarm clock

60

[ ] Artificial vision products such as eSight or OrCam

[ ] Braille note-taker

[ ] Brailler, Slate or Stylus

[ ] Intervenors for the deafblind

[ ] Sign language interpreters

[ ] Assistive listening device such as a hearing aid or FM system

[ ] Specialized transportation system

[ ] White cane

[ ] Guide dog

[ ] Mobility aid, such as crutches, walkers

[ ] Low-tech aids for mobility and independent living

[ ] Low-tech vision aids (monoculars, bioptics, etc.)

[ ] Other (please specify): _________________________________________________

________________________________________

How You Use Technology (Continued...)

8) Do you use a smartphone?

( ) Yes

( ) No


9) How do you use your smartphone? Please check all that apply.

[ ] I use it for my job

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[ ] I use it for getting around

[ ] I use it for recreation (reading, watching TV/movies, listening to music/podcasts, etc.)

[ ] I use it for household chores (grocery lists, etc.)

[ ] I use it as a personal calendar


10) Which of the following apps, if any, do you use on daily basis. Please check all that apply.

[ ] LookTel Money Reader

[ ] SayText

[ ] Color Identifier

[ ] TalkingTag LV

[ ] Learning Ally

[ ] Visible Braille

[ ] Navigon MobileNavigator

[ ] Big Clock

[ ] The Talking Calculator

[ ] iBlink Radio

[ ] Ideal Accessibility Installer

[ ] ScanLife Barcode and QR Reader

[ ] Magnify

[ ] Messagease Keyboard

[ ] Font Installer Root

[ ] Ultra Magnifier +

[ ] Walky Talky

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[ ] Classic Text To Speech Engine

[ ] NoLED

[ ] BlindSquare

[ ] Key2Access

[ ] KNFBReader

[ ] Seeing AI


11) Which of the following apps or tools, if any, do you use on daily basis for getting around.


[ ] Nearby Explorer for Android

[ ] The Seeing Eye GPS

[ ] BlindSquare

[ ] Open Street Maps

[ ] Yelp

[ ] Aira

[ ] Seeing AI

[ ] Google maps

[ ] Built-in GPS that come as part of my smart phone

[ ] Identification cane

[ ] Support cane

[ ] White cane

[ ] Guide dog


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________________________________________

Land Transport

12) What are your most common trip purposes? Please check all that apply.

[ ] Visiting family or friends

[ ] Grocery shopping

[ ] Shopping (for items other than groceries)

[ ] Walking for recreation

[ ] Exercise

[ ] Doctors appointments

[ ] Entertainment or leisure activities

[ ] Religious

[ ] School

[ ] Work


13) How often do you travel by car for daily trips?

( ) Never

( ) Rarely (once monthly or less)

( ) Sometimes (2 to 5 times per month)

( ) Frequently (2 to 5 times per week)

( ) All the time (every day)

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14) How often do you take a Taxi, Uber, or Lyft for daily trips?

( ) Never





15) How often do you walk for daily trips?

( ) Never





16) How often do you travel by bicycle for daily trips?

( ) Never





17) How often do you use public transport (Bus, Subway, LRT, etc.) for daily trips?

( ) Never


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18) How often do you travel by para-transit for daily trips?

( ) Never





________________________________________

Land Transport (Continued...)

19) How do you usually find your way around in your own community? Please check all that

apply.

[ ] Using Smartphone apps

[ ] Using GPS devices

[ ] Using a white cane

[ ] Using a guide dog

[ ] Asking friends or family to travel with you

[ ] Independently travel with memorizing routes

[ ] Mostly travel independently, but sometimes ask for help from others to make sure that I am

on the right path

66


20) How do you usually find your way around in an unfamiliar area (other than your own

community)? Please check all that apply.

[ ] Using Smartphone apps

[ ] Using GPS devices

[ ] Using a white cane

[ ] Using a guide dog

[ ] Asking friends or family to travel with you

[ ] Independently travel with memorizing routes

[ ] Mostly travel independently, but sometimes ask for help from others to make sure that I am

on the right path


21) Please rate your satisfaction with public transport accessibility in your area?

( ) I don't have access to public transport

( ) Extremely dissatisfied

( ) Very dissatisfied

( ) Somewhat dissatisfied

( ) Somewhat satisfied

( ) Very satisfied

( ) Extremely satisfied

22) Please rate your satisfaction with para-transit system accessibility in your area?

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( ) I don't have access to para-transit

( ) Extremely dissatisfied

( ) Very dissatisfied

( ) Somewhat dissatisfied

( ) Somewhat satisfied

( ) Very satisfied

( ) Extremely satisfied

________________________________________

Land Transport (Continued...)

23) As a pedestrian on a sidewalk, it is important to hear vehicle noise or other kinds of noise

from traffic.

( ) Yes

( ) No

24) As a pedestrian, it is important to hear warnings from cyclists both on sidewalks and on

roads.

( ) Yes

( ) No

25) As a pedestrian I think audible sounds from Accessible Pedestrian Signals (APS) are helpful

for crossing streets.

( ) Yes

( ) No

68

26) As a pedestrian, it is important to hear vehicle noise when you and a vehicle approach an

intersection?

( ) Yes

( ) No

27) Do you use headphones while walking on a sidewalk or crossing streets?

( ) Yes

( ) No

28) What is the most common purpose for using headphone while walking on a sidewalk or

crossing streets?

( ) Listening to music

( ) Using navigational apps

( ) Other (please specify): _________________________________________________

29) What are the reasons you do not prefer to use headphones while walking on a sidewalk or

crossing streets? Please check all that apply.

[ ] Safety issues

[ ] Isolate from surrounding environment

[ ] Headphones are easy to be lost


30) When making the decision to cross, which of the following are most important to you:

(Please check all that apply)

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[ ] I prefer to rely on my sense of hearing when vehicles stop before deciding when to cross

[ ] I prefer to rely on audible sounds from Accessible Pedestrian Signals before deciding when to

cross

[ ] I use my remaining eyesight to help me identify cars, bicycles, and other pedestrians

[ ] I prefer to use technologies or apps such as eSight, Be My Eyes, etc.


________________________________________

Electric Vehicles

31) Can you distinguish the noises made by individual motorized devices such as scooters or

electric bikes on sidewalks versus motor vehicles on roads?

( ) Yes

( ) No

32) Have you ever heard about "Electric Vehicles"?

( ) Yes

( ) No

33) Have you ever had an accident or near accident with an "Electric Vehicle"?

( ) Yes

( ) No

34) Nowadays "Electric Vehicles" have become more popular on streets. As these vehicles

operate quietly, do you think that they impact your safety as a pedestrian?

70

( ) Yes

( ) No

________________________________________

Self-driving/Automated and Connected Automated Vehicles

35) Have you ever heard about "Self-driving", "Automated", "Autonomous" and/or "Connected

Autonomous" vehicles?

( ) Yes

( ) No

36) Do you think "Self-driving" vehicles impact your independence for traveling?

( ) Yes

( ) No

37) As a pedestrian, how much do you trust "Self-driving" vehicles?

( ) Not at all

( ) Barely

( ) Somewhat

( ) A lot

( ) Entirely

( ) I don't know

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38) Consider as a passenger of a vehicle such as an Uber or a bus that cruising without a driver.

Please rate your preference for using these "Self-driving" vehicles in the near future?

( ) Not preferred at all

( ) Somewhat preferred

( ) Highly preferred

( ) I don't know

39) What, if any, concerns do you have about sharing the road with "Self-driving" vehicles?


[ ] Technology failures that will affect negatively the safety for walking areas.

[ ] Lack of proper communication techniques between self-driving vehicles and other road users

such as pedestrians.

[ ] The possibility of new disastrous situations with self-driving vehicles. For example, in

complex cases when self-driving vehicles have to decide to collide with pedestrians or other

vehicles on the road or safety priority for the passengers.


________________________________________

Self-driving/Automated and Connected Automated Vehicles (Continued...)

40) Do you agree/disagree with the following statement:

I will use a navigation device which provides real-time information (such as smart watches,

wearable cameras, etc.), if it is not connected to other devices or a third party.

( ) Agree

( ) Disagree

( ) I don't know

72

41) Do you agree/disagree with the following statement:

I prefer to have the option to use a navigation device whether it is connected to other devices or a

third party or not. Transportation systems should support my safety and security regardless of

whether I have or use an additional device or not.

( ) Agree

( ) Disagree

( ) I don't know

42) How do you prefer to communicate with "Connected Automated" vehicles or "Self-driving"

vehicles when you want to cross streets?

( ) Audible alerts from the infrastructure such as Accessible Pedestrian Signal alerts.

( ) Alerts from the Self-driving vehicles that announce their current status, such as the Self-

driving vehicle is about to stop for you.

( ) Alerts from wearable devices such as eSight about the Self-driving vehicle's current status,

such as the vehicle is about to stop.

( ) I prefer not to communicate with Self-driving vehicles, their systems should be accurate in

detecting pedestrians and stopping for them.


________________________________________

Some Questions About You

43) Which of the following best describes your household?

( ) One adult

( ) Two adults

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( ) One adult and a child

( ) One adult and more than a child

( ) Two adults and a child

( ) Two adults and more than a child


44) How many individuals in your home hold a valid drivers license?

( ) Zero

( ) One

( ) Two

( ) Three

( ) More than three

45) How many vehicles are owned by persons living in your household?

( ) Zero

( ) One

( ) Two

( ) Three

( ) More than three

46) Would you identify as:


( ) Male

( ) Female

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( ) Another gender identity: _________________________________________________

47) Age:

_________________________________________________

48) What is your highest level of education?


( ) Less than a high school diploma

( ) High school diploma or equivalent

( ) CEGEP

( ) College

( ) Trade certificate or professional certification

( ) Bachelor's degree

( ) Master's degree

( ) Doctoral or Professional degree

49) What best describes your current employment status?


( ) Employed full time

( ) Employed part time

( ) Unemployed

( ) Self-employed

( ) Retired

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( ) Student

( ) Unable to work


________________________________________

Future Research

50) Please rate the accessibility level of this survey?

( ) Excellent

( ) Very good

( ) Good

( ) Average

( ) Poor

51) Do you want to participate in further research conducted by the CNIB?

( ) Yes

( ) No

52) Your responses have been saved to our database.

On the next screen, we will invite you to provide your contact information should you wish to

receive a copy of the survey analysis and CNIB's final report.

We hope to have this report completed late in 2019.

Contact us at [email protected]

Full Name: _________________________________________________

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Email Address: _________________________________________________

Please send me future notices about CNIB advocacy, research or community events.

( ) Yes

( ) No

________________________________________

Documents

Understanding the Impacts of Connected Autonomous Vehicles … · 2019. 11. 27. · Although, there is a lot of research on different aspects of connected and autonomous vehicles